Trainer Characteristics

Machine learning task

Regression

Is normalization required?

Yes

Is caching required?

No

Required NuGet in addition to Microsoft.ML

Microsoft.ML.Mkl.Components

Exportable to ONNX

Yes

Training Algorithm Details

Ordinary least squares (OLS) is a parameterized regression method.
It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables.
The regression parameters can be estimated by minimizing the squares of the difference between observed values and the predictions

Extension Methods

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against
cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView)
is called. It is often important for an estimator to return information about what was fit, which is why the
Fit(IDataView) method returns a specifically typed object, rather than just a general
ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines
with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the
estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this
method attach a delegate that will be called once fit is called.